- 01A launch gate that maps each AI feature to its input trust level, tool authority scope, and tenant isolation model catches the majority of misconfigurations before they reach users.
- 02The gate blocks on three conditions. Untrusted input reaching a model with tool access without isolation. Any tool whose worst case action is irreversible and lacks a human approval step. Missing per tenant token budget configuration.
- 03Threat model artifacts from the gate feed directly into your SIEM runbooks so that detection and response engineers have context at alert time rather than discovering it during an incident.
- 04Ownership must be explicit. Assign a named AI security reviewer to each feature and track gate completion in the same system you use for vulnerability remediation.
Every production AI feature introduces a new trust boundary. The model receives input from somewhere, decides what to do, and optionally calls tools with real authority. When those three elements combine without deliberate security design, the result is a surface that neither classic application security nor network security covers well.
A launch gate solves this problem the same way a preparatory security review solves it for databases or payment flows. It is a structured checklist with pass or fail outcomes, a named reviewer, and a formal sign off artifact that feeds downstream detection and response processes.
Concrete gate criteria for every AI feature
The gate should be short enough that a team can complete it in under two hours. Long checklists get skipped. The following criteria cover the failure modes that appear most often in production AI systems.
/AI_LAUNCH_GATE_CRITERIA
| Criterion | Pass Condition | Block Condition |
|---|---|---|
| Input trust classification | All input sources labeled trusted or untrusted | Any source with unknown trust level |
| Isolation architecture | Untrusted text reaches only a view only model | Untrusted text reaches a model with tool access directly |
| Tool authority inventory | Every tool has a worst case impact statement | Any tool without a documented scope |
| Irreversible action gate | Human or policy approval required for all irreversible actions | Any irreversible action with no approval gate |
| Tenant isolation | Retrieval and tool calls are scoped per tenant identity | Shared retrieval context across tenant boundaries |
| Token budget | Per tenant per day hard cap configured | No budget or soft warning only |
| Audit log destination | All model inputs, outputs, and tool calls ship to SIEM | Logs written to local disk only or discarded |
Operational workflow for running a gate
The gate runs as a structured meeting with three participants. The feature engineer who built the AI component, the security reviewer who owns the AI security program, and the product manager who controls the launch decision. The meeting should produce a single artifact. A completed gate form with a pass or conditional pass outcome and any blocking findings listed as P1 tickets.
Conditional pass is a valid outcome. It means the feature can proceed to a limited rollout (for example, internal users or a fixed percentage of traffic) while blocking findings are resolved. This prevents the gate from becoming a veto tool that slows teams down without improving security.
Gate workflow in four steps.
Run the gate as close to feature completion as possible, not on the day of launch. Late gates produce rushed outcomes.
- ▸Step 1. Feature engineer completes the gate form before the meeting, filling in input sources, tool list, and isolation architecture.
- ▸Step 2. Security reviewer reads the form, assigns pass or fail to each criterion, and lists blocking findings.
- ▸Step 3. Meeting resolves ambiguous items and converts blocking findings to tickets with P1 priority.
- ▸Step 4. Gate form is stored in the threat model registry and linked to the deployment record.
Metrics that prove the gate is functioning
A gate that teams bypass or rubber stamp is worse than no gate, because it creates a false sense of coverage. Track these four metrics monthly to detect gate drift.
- 01Gate completion rate. Percentage of AI features that reached production with a completed gate form. Target is 100 percent.
- 02Blocking finding resolution time. Median days from gate to blocking ticket closure. Target is under 14 days.
- 03Post launch finding rate. AI security findings discovered after production launch that should have been caught at the gate. Target is trending toward zero.
- 04Reviewer coverage. Number of distinct security reviewers completing gates. A single reviewer is a program risk.
Making the gate a program asset, not a ceremony
The gate form is a threat model artifact. Every entry in it, the input sources, the tool list, the isolation decisions, should be queryable by the detection and response team. When an alert fires on an AI related event, the first thing a responder needs is context about what the feature was designed to do, what tools it had access to, and what tenant it was operating in.
Build a lightweight registry that maps each AI feature slug to its gate form. Most teams store this as a structured JSON file in the same repository as the feature code. The SIEM can reference it at alert time through an enrichment lookup. That single integration transforms a compliance artifact into an operational tool that pays for itself the first time an analyst uses it to scope an incident in minutes rather than hours.